Gene selection for tumor classification using neighborhood rough sets and entropy measures

Abstract

With the development of bioinformatics, tumor classification from gene expression data becomes an important useful technology for cancer diagnosis. Since a gene expression data often contains thousands of genes and a small number of samples, gene selection from gene expression data becomes a key step for tumor classification. Attribute reduction of rough sets has been successfully applied to gene selection field, as it has the characters of data driving and requiring no additional information. However, traditional rough set method deals with discrete data only. As for the gene expression data containing real-value or noisy data, they are usually employed by a discrete preprocessing, which may result in poor classification accuracy. In this paper, we propose a novel gene selection method based on the neighborhood rough set model, which has the ability of dealing with real-value data whilst maintaining the original gene classification information. Moreover, this paper addresses an entropy measure under the frame of neighborhood rough sets for tackling the uncertainty and noisy of gene expression data. The utilization of this measure can bring about a discovery of compact gene subsets. Finally, a gene selection algorithm is designed based on neighborhood granules and the entropy measure. Some experiments on two gene expression data show that the proposed gene selection is an effective method for improving the accuracy of tumor classification.

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

“…In recent decades, rough sets and fuzzy-rough sets theories have been employed in various application areas such as data mining [4,5,86,[301][302][303], software packages [141,304], web ontology [138,[305][306][307], pattern recognition [24,148,187,[308][309][310], granular computing [38,221,238,251], genetic algorithm [310][311][312][313], prototype selection [145,163], solid transportation [146,314,315], social networks [316][317][318], artificial neural network [92,153,319], remote sensing [320,321], and gene selection [158,[322][323][324]]. An et al [140] analysed a regression algorithm based on fuzzy partition, fuzzy-rough sets, estimation of regression values, and fuzzy approximation for estimating wind speed.…”

A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures

“…Moreover, the original property of the continuous-valued data will change after discretization, and some useful information will be lost [ 8 ]. To overcome this drawback, scholars have developed many extensions for the traditional rough set model [ 19 , 20 ]. As an extended rough set model, neighborhood rough set model is introduced to solve the problem that classical rough sets cannot handle continuous numerical data.…”

Transcription Direction Patterns of Adjacent Genes in Mycobacterium Tuberculosis Using GENAVIS

“…The understanding of gene neighbourhood has been applied in several studies; it was used as entropy measure under the frame of neighborhood rough sets to develop a novel gene selection method for tackling the uncertainty and noisy of gene expression data [3]. Their gene selection model was applied on tumor classification for the discovery of compact gene subsets with improved accuracy.…”